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UseCase

AI Governance Framework for Banking

PiTech has served federal agencies and defense contractors for 18+ years delivering CMMI Level 3-disciplined cloud authorization, Zero Trust, legacy modernization, and workflow automation programs that satisfy FISMA, FedRAMP, and CMMC 2.0 requirements

70%+

Banks use AI in production

60 days

Full model inventory delivered

SR 11-7

Model risk management aligned

Board-ready

AI risk dashboard

Client Snapshot

Industry

Banking & Financial Services

Solution

AI, GenAI & ML | IT Consulting

Complexity

High

Delivery

Advisory + Program Design

The Problem

More than 70% of banking firms report using AI in some capacity, yet only 12.2% describe their AI and machine learning strategy as well-defined and resourced. The gap between adoption and governance is the defining risk profile of 2026. OCC, Federal Reserve, and FDIC examiners are actively probing AI governance maturity in routine and targeted examinations institutions without model inventories, validation documentation, and board-level AI reporting are creating examination exposure.

The vendor dimension is where most bank AI governance programs fail. When a bank activates an AI feature embedded in a SaaS platform or consumes a foundation model via API, that system carries model risk the bank is responsible for. Most institutions discover 30–50% more AI footprint than their initial assessment suggests and most of the undiscovered footprint is vendor-embedded.

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12.2%

of financial institutions describe their AI and ML strategy as well-defined and resourced, per industry surveys. The remaining 87.8% are operating AI in production without a governance framework commensurate with SR 11-7 and OCC AI guidance and examiners are now looking for it.

How PiTech Delivers

01

AI Inventory and Footprint Discovery

Complete AI system inventory across all sourcing categories: internally built models, licensed AI platforms, SaaS products with embedded AI features, and vendor API integrations. Typically reveals 30–50% more AI footprint than the institution’s prior self-assessment.

02

Risk Classification and Tiering

Each AI system classified against SR 11-7, OCC AI guidance, CFPB fair lending, and UDAAP requirements. High-risk tiers include credit decisioning, BSA/AML processes, and customer-facing automation with consequential effects on consumers.

03

Validation Framework and Vendor Governance

Model validation playbooks calibrated to AI/ML use case categories: predictive, generative, and agentic. Vendor AI addendum templates for existing supplier contracts covering training data use, model change notification, subprocessor disclosure, and audit rights.

04

Board Reporting and Continuous Monitoring

AI governance dashboard with quarterly board-level reporting cadence. Incident response procedures for AI-specific failure modes: hallucination escalation, model drift events, prompt injection findings, and agentic system failures.

Proven Outcomes

60 days

To deliver complete AI model inventory including vendor-embedded AI

30–50%

More AI footprint typically discovered vs. prior self-assessment

18+ yrs

Banking technology experience SR 11-7, OCC, and CFPB expertise

Proven Outcomes

18+

Years in Regulated Industries

What You Gain

100%

AI model inventory completed within 60 days including vendor-embedded AI

Risk-tiered

AI portfolio with defined validation requirements per classification tier

Exam-ready

Vendor governance and model documentation package for examiner review

Quarterly

Board AI governance dashboard with defined reporting cadence

What's Included

AI inventory methodology

AI inventory methodology

Discovery questionnaires, integration mapping, and shadow AI detection across all sourcing categories

Risk tiering framework

Risk tiering framework

Classification matrix aligned to SR 11-7, OCC AI guidance, CFPB fair lending, and UDAAP

Model validation playbooks

Model validation playbooks

Predictive, generative, and agentic AI validation approaches with behavioral testing protocols

Vendor AI addendum templates

Vendor AI addendum templates

Contract language covering training data use, model change notification, and audit rights

Board AI governance dashboard

Board AI governance dashboard

Quarterly reporting template with governance KPIs and risk trend visualization

AI incident response procedures

Fair lending monitoring module

AI incident response procedures

false-positive disparity tracking by demographic proxy with defined alert thresholds

Pre-examination self-assessment

Pre-examination self-assessment

Self-scoring against OCC and Fed AI examiner expectations with remediation roadmap

Frequently Asked Questions

How does PiTech define "AI" for inventory purposes does it include vendor-embedded AI?

Yes. PiTech’s inventory captures internally built models, licensed AI platforms, SaaS products with AI features, and vendor API integrations. The majority of shadow AI exposure sits in vendor-embedded categories that standard model inventories miss. Most banks discover 30–50% more AI footprint than they initially estimated.
OCC examiners are probing for: a current, complete model inventory; risk classification documentation with tiering rationale; evidence that high-risk AI models have been validated; vendor contracts with AI-specific terms; and board-level AI risk reporting. PiTech delivers each of these as defined program deliverables.
Both. PiTech offers governance framework design as standalone advisory and model validation execution as a managed service for institutions lacking internal AI validation capacity for AI-specific models. Many regional banks use PiTech’s team as their independent validation function for AI models.
Agentic systems take multi-step actions autonomously the locus of human oversight shifts from individual decisions to workflow design. MRM frameworks must expand to cover agent authority boundaries, approval thresholds, and escalation protocols. Most bank MRM frameworks have not yet been updated for agentic systems. PiTech includes agentic AI governance in every program we build.
A governance framework covering inventory, risk classification, validation playbooks, vendor governance, and board reporting can be designed and operationalized in 12–16 weeks for a mid-size regional bank. The initial 60-day model inventory is the first deliverable providing immediate examination readiness while the full program is built.

AI governance is not optional in 2026. PiTech builds programs that satisfy examiners, protect customers, and enable continued adoption.

Contact PiTech to begin with a governance maturity assessment specific to your model portfolio and regulatory environment.
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